A plume-in-grid approach to characterize air quality impacts of aircraft emissions at the Hartsfield-Jackson Atlanta International Airport
Bibliographic record
Abstract
Abstract. This study examined the impacts of aircraft emissions during the landing and takeoff cycle on PM2.5 concentrations during the months of June 2002 and July 2002 at the Hartsfield-Jackson Atlanta International Airport. Primary and secondary pollutants were modeled using the Advanced Modeling System for Transport, Emissions, Reactions, and Deposition of Atmospheric Matter (AMSTERDAM). AMSTERDAM is a modified version of the Community Multiscale Air Quality (CMAQ) model that incorporates a plume-in-grid process to simulate emissions sources of interest at a finer scale than can be achieved using CMAQ's model grid. Three fundamental issues were investigated: the effects of aircraft on PM2.5 concentrations throughout northern Georgia, the differences resulting from use of AMSTERDAM's plume-in-grid process rather than a traditional CMAQ simulation, and the concentrations observed in aircraft plumes at sub-grid scales. Comparison of model results with an air quality monitor located in the vicinity of the airport found that normalized mean bias ranges from −77.5% to 6.2% and normalized mean error ranges from 40.4% to 77.5%, varying by species. Aircraft influence average PM2.5 concentrations by up to 0.232 μg m−3 near the airport and by 0.001–0.007 μg m−3 throughout the Atlanta metro area. The plume-in-grid process increases concentrations of secondary PM pollutants by 0.005–0.020 μg m−3 (compared to the traditional grid-based treatment) but reduces the concentration of non-reactive primary PM pollutants by up to 0.010 μg m−3, with changes concentrated near the airport. Examination of sub-grid scale results indicates that puffs within 20 km of the airport often have average PM2.5 concentrations one order of magnitude higher than aircraft contribution to the grid cells containing those puffs, and within 1–4 km of emitters, puffs may have PM2.5 concentrations 3 orders of magnitude greater than the aircraft contribution to their grid cells. 21% of all aircraft-related puffs from the Atlanta airport have at least 0.1 μg m−3 PM2.5 concentrations. Median daily puff concentrations vary between 0.017 and 0.134 μg m−3, while maximum daily puff concentrations vary between 6.1 and 42.1 μg m−3 during the 2-month period. In contrast, median daily grid concentrations vary between 0.015 and 0.091 μg m−3, while maximum daily grid concentrations vary between 0.751 and 2.55 μg m−3. Future researchers may consider using AMSTERDAM to understand the impacts of aircraft emissions at other airports, for proposed future airports, for airport expansion projects under various future scenarios, and for other national-scale studies specifically when the maximum impacts at fine scales are of interest.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".